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from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestPairWiseLossOps(serial.SerializedTestCase):
@given(X=hu.arrays(dims=[2, 1],
elements=hu.floats(min_value=0.0, max_value=10.0)),
label=hu.arrays(dims=[2, 1],
elements=st.integers(min_value=0, max_value=1),
dtype=np.float32),
**hu.gcs_cpu_only)
def test_pair_wise_loss_predictions(self, X, label, gc, dc):
workspace.FeedBlob('X', X)
workspace.FeedBlob('label', label)
new_label = np.array([label[1], label[0]])
new_x = np.array([X[1], X[0]])
workspace.FeedBlob('new_x', new_x)
workspace.FeedBlob('new_label', new_label)
net = core.Net('net')
net.PairWiseLoss(['X', 'label'], ['output'])
net.PairWiseLoss(['new_x', 'new_label'], ['new_output'])
plan = core.Plan('predict_data')
plan.AddStep(core.execution_step('predict_data',
[net], num_iter=1))
workspace.RunPlan(plan)
output = workspace.FetchBlob('output')
new_output = workspace.FetchBlob('new_output')
sign = 1 if label[0] > label[1] else -1
if label[0] == label[1]:
self.assertEqual(np.asscalar(output), 0)
return
self.assertAlmostEqual(
np.asscalar(output),
np.asscalar(np.log(1 + np.exp(sign * (X[1] - X[0])))),
delta=1e-4
)
# check swapping row order doesn't alter overall loss
self.assertAlmostEqual(output, new_output)
@given(X=hu.arrays(dims=[2, 1],
elements=hu.floats(min_value=0.0, max_value=10.0)),
label=hu.arrays(dims=[2, 1],
elements=st.integers(min_value=0, max_value=1),
dtype=np.float32),
dY=hu.arrays(dims=[1],
elements=hu.floats(min_value=1, max_value=10)),
**hu.gcs_cpu_only)
def test_pair_wise_loss_gradient(self, X, label, dY, gc, dc):
workspace.FeedBlob('X', X)
workspace.FeedBlob('dY', dY)
workspace.FeedBlob('label', label)
net = core.Net('net')
net.PairWiseLossGradient(
['X', 'label', 'dY'],
['dX'],
)
plan = core.Plan('predict_data')
plan.AddStep(core.execution_step('predict_data',
[net], num_iter=1))
workspace.RunPlan(plan)
dx = workspace.FetchBlob('dX')
sign = 1 if label[0] > label[1] else -1
if label[0] == label[1]:
self.assertEqual(np.asscalar(dx[0]), 0)
return
self.assertAlmostEqual(
np.asscalar(dx[0]),
np.asscalar(-dY[0] * sign / (1 + np.exp(sign * (X[0] - X[1])))),
delta=1e-2 * abs(np.asscalar(dx[0])))
self.assertEqual(np.asscalar(dx[0]), np.asscalar(-dx[1]))
delta = 1e-3
up_x = np.array([[X[0] + delta], [X[1]]], dtype=np.float32)
down_x = np.array([[X[0] - delta], [X[1]]], dtype=np.float32)
workspace.FeedBlob('up_x', up_x)
workspace.FeedBlob('down_x', down_x)
new_net = core.Net('new_net')
new_net.PairWiseLoss(['up_x', 'label'], ['up_output'])
new_net.PairWiseLoss(['down_x', 'label'], ['down_output'])
plan = core.Plan('predict_data')
plan.AddStep(core.execution_step('predict_data', [new_net], num_iter=1))
workspace.RunPlan(plan)
down_output_pred = workspace.FetchBlob('down_output')
up_output_pred = workspace.FetchBlob('up_output')
np.testing.assert_allclose(
np.asscalar(dx[0]),
np.asscalar(
0.5 * dY[0] *
(up_output_pred[0] - down_output_pred[0]) / delta),
rtol=1e-2, atol=1e-2)
@serial.given(n=st.integers(0, 10), k=st.integers(1, 5), **hu.gcs_cpu_only)
def test_pair_wise_loss_batch(self, n, k, gc, dc):
lengths = np.random.randint(k, size=n).astype(np.int32) + 1
X = np.random.rand(sum(lengths)).astype(np.float32)
label = np.random.randint(k, size=sum(lengths)).astype(np.float32)
def pair_wise_op(X, label, lengths):
N = lengths.size
output = np.zeros(N).astype(np.float32)
def f(x):
return np.log(1 + np.exp(x))
offset = 0
for idx in range(N):
offset += lengths[idx - 1] if idx > 0 else 0
count = 0
for i in range(offset, offset + lengths[idx]):
for j in range(offset, i):
if label[i] == label[j]:
continue
sign = 1 if label[i] > label[j] else -1
output[idx] += f(sign * (X[j] - X[i]))
count += 1
if count > 0:
output[idx] /= count
return [output]
op = core.CreateOperator(
'PairWiseLoss',
['X', 'label', 'lengths'],
'out'
)
# Check against numpy reference
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X, label, lengths],
reference=pair_wise_op,
)
# Check over multiple devices
self.assertDeviceChecks(dc, op, [X, label, lengths], [0])
# Gradient check
self.assertGradientChecks(gc, op, [X, label, lengths], 0, [0])
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